Show simple item record

dc.contributor.advisorRuozzi, Nicholas
dc.creatorSmashnov, Uri
dc.date.accessioned2018-08-03T16:59:20Z
dc.date.available2018-08-03T16:59:20Z
dc.date.created2018-05
dc.date.issued2018-05
dc.date.submittedMay 2018
dc.identifier.urihttp://hdl.handle.net/10735.1/5918
dc.description.abstractIn this thesis, we focus on the Markov Random Field graph. We gradually transition to Hidden Markov Model and work exclusively with binary graphs. We then utilize produced graphical model to train machine learning model that would classify images of written digits. The thesis is comprised of two parts: review of the articles and implementation (coding) of the key models and methods introduced in the articles. The articles are chosen from the seminal work as well as from the recent advances in graphical models, graphical models with latent variables and their application to various image recognition problems. The last part of the thesis applies developed inference framework to produce machine learning model for written digits recognition task.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rights©2018 The Author. Digital access to this material is made possible by the Eugene McDermott Library. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
dc.subjectMarkov random fields
dc.subjectGraph theory
dc.subjectHidden Markov models
dc.subjectMachine learning
dc.subjectGraphic methods
dc.titleVariational Methods for Graph Models with Hidden Variables
dc.typeThesis
dc.date.updated2018-08-03T16:59:20Z
dc.type.materialtext
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.departmentComputer Science
thesis.degree.levelMasters
thesis.degree.nameMSCS


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record